Rapid instructed task learning: a new window into the human brain's unique capacity for flexible cognitive control

Dynamic changes in task preparation in a multi-task environment: The task transformation paradigm

A key element of human flexible behavior concerns the ability to continuously predict and prepare for sudden changes in tasks or actions. Here, we tested whether people can dynamically modulate task preparation processes and decision-making strategies when the identity of a to-be-performed task becomes uncertain. To this end, we developed a new paradigm where participants need to prepare for one of nine tasks on each trial. Crucially, in some blocks, the task being prepared could suddenly shift to a different task after a longer cue-target interval, by changing either the stimulus category or categorization rule that defined the initial task. We found that participants were able to dynamically modulate task preparation in the face of this task uncertainty. A second experiment shows that these changes in behavior were not simply a function of decreasing task expectancy, but rather of increasing switch expectancy. Finally, in the third and fourth experiment, we demonstrate that these dynamic modulations can be applied in a compositional manner, depending on whether either only the stimulus category or categorization rule would be expected to change.

Concurrent response and action effect representations across the somatomotor cortices during novel task preparation

Instructions allow us to fulfill novel and complex tasks on the first try. This skill has been linked to preparatory brain signals that encode upcoming demands in advance, facilitating novel performance. To deepen insight into these processes, we explored whether instructions pre-activated task-relevant motoric and perceptual neural states. Critically, we addressed whether these representations anticipated activity patterns guiding overt sensorimotor processing, which could reflect that internally simulating novel tasks facilitates the preparation. To do so, we collected functional magnetic resonance imaging data while female and male participants encoded and implemented novel stimulus-response associations. Participants also completed localizer tasks designed to isolate the neural representations of the mappings-relevant motor responses, perceptual consequences, and stimulus categories. Using canonical template tracking, we identified whether and where these sensorimotor representations were pre-activated. We found that response-related templates were encoded in advance in regions linked with action control, entailing not only the instructed responses but also their somatosensory consequences. This result was particularly robust in primary motor and somatosensory cortices. While, following our predictions, we found a systematic decrease in the irrelevant stimulus templates' representational strength compared to the relevant ones, this difference was due to below-zero estimates linked to the irrelevant category activity patterns. Overall, our findings reflect that instruction processing relies on the sensorimotor cortices to anticipate motoric and kinesthetic representations of prospective action plans, suggesting the engagement of motor imagery during novel task preparation. More generally, they stress that the somatomotor system could participate with higher-level frontoparietal regions during anticipatory task control.

The neuroscience of active learning and direct instruction

Throughout the educational system, students experiencing active learning pedagogy perform better and fail less than those taught through direct instruction. Can this be ascribed to differences in learning from a neuroscientific perspective? This review examines mechanistic, neuroscientific evidence that might explain differences in cognitive engagement contributing to learning outcomes between these instructional approaches. In classrooms, direct instruction comprehensively describes academic content, while active learning provides structured opportunities for learners to explore, apply, and manipulate content. Synaptic plasticity and its modulation by arousal or novelty are central to all learning and both approaches. As a form of social learning, direct instruction relies upon working memory. The reinforcement learning circuit, associated agency, curiosity, and peer-to-peer social interactions combine to enhance motivation, improve retention, and build higher-order-thinking skills in active learning environments. When working memory becomes overwhelmed, additionally engaging the reinforcement learning circuit improves retention, providing an explanation for the benefits of active learning. This analysis provides a mechanistic examination of how emerging neuroscience principles might inform pedagogical choices at all educational levels.

The dynamics of functional brain network segregation in feedback-driven learning

Prior evidence suggests that increasingly efficient task performance in human learning is associated with large scale brain network dynamics. However, the specific nature of this general relationship has remained unclear. Here, we characterize performance improvement during feedback-driven stimulus-response (S-R) learning by learning rate as well as S-R habit strength and test whether and how these two behavioral measures are associated with a functional brain state transition from a more integrated to a more segregated brain state across learning. Capitalizing on two separate fMRI studies using similar but not identical experimental designs, we demonstrate for both studies that a higher learning rate is associated with a more rapid brain network segregation. By contrast, S-R habit strength is not reliably related to changes in brain network segregation. Overall, our current study results highlight the utility of dynamic functional brain state analysis. From a broader perspective taking into account previous study results, our findings align with a framework that conceptualizes brain network segregation as a general feature of processing efficiency not only in feedback-driven learning as in the present study but also in other types of learning and in other task domains.

Task learning is subserved by a domain-general brain network

One of the most important human faculties is the ability to acquire not just new memories but the capacity to perform entirely new tasks. However, little is known about the brain mechanisms underlying the learning of novel tasks. Specifically, it is unclear to what extent learning of different tasks depends on domain-general and/or domain-specific brain mechanisms. Here human subjects (n = 45) learned to perform 6 new tasks while undergoing functional MRI. The different tasks required the engagement of perceptual, motor, and various cognitive processes related to attention, expectation, speed-accuracy tradeoff, and metacognition. We found that a bilateral frontoparietal network was more active during the initial compared with the later stages of task learning, and that this effect was stronger for task variants requiring more new learning. Critically, the same frontoparietal network was engaged by all 6 tasks, demonstrating its domain generality. Finally, although task learning decreased the overall activity in the frontoparietal network, it increased the connectivity strength between the different nodes of that network. These results demonstrate the existence of a domain-general brain network whose activity and connectivity reflect learning for a variety of new tasks, and thus may underlie the human capacity for acquiring new abilities.

Instructing somebody else to act: motor co-representations in the instructor

Instructions enable humans to perform novel tasks quickly. This is achieved by creating and activating the instruction representation for upcoming tasks, which can then modulate ongoing task behaviour in an almost 'reflexive' manner, an effect called instruction-based reflexivity. While most research has focused on understanding how verbal instructions are represented within the 'instructed' (i.e. the person receiving instructions), here we focus on how the instructor's (i.e. the person giving instructions) behaviour is affected through instructing. In a series of three experiments and one pooled analysis, we extended the classical instruction-based reflexivity paradigm to a novel social variant in which the instructions are given by an instructor (rather than visual computer-generated instructions). We found an instruction-based reflexivity effect for the instructor, that is, the instructor's task performance was better on congruent compared to incongruent trials (i.e. Experiments 1 and 2, pooled analysis). This suggests that the instructor represents the instructions of the instructed in an action-oriented format. However, this did not depend on the specific task of the instructed (i.e. Experiment 1), nor is it exclusively social (i.e. Experiment 3).

Abstract representations emerge in human hippocampal neurons during inference behavior

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization 1 . However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning, and how they relate to behavior 2,3 . Here we characterized the representational geometry of populations of neurons (single-units) recorded in the hippocampus, amygdala, medial frontal cortex, and ventral temporal cortex of neurosurgical patients who are performing an inferential reasoning task. We find that only the neural representations formed in the hippocampus simultaneously encode multiple task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consisted of disentangled directly observable and discovered latent task variables. Interestingly, learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behavior suggests that abstract/disentangled representational geometries are important for complex cognition.

Planning ahead: Predictable switching recruits task-active and resting-state networks

Switching is a difficult cognitive process characterised by costs in task performance; specifically, slowed responses and reduced accuracy. It is associated with the recruitment of a large coalition of task-positive regions including those referred to as the multiple demand cortex (MDC). The neural correlates of switching not only include the MDC, but occasionally the default mode network (DMN), a characteristically task-negative network. To unpick the role of the DMN during switching we collected fMRI data from 24 participants playing a switching paradigm that perturbed predictability (i.e., cognitive load) across three switch dimensions-sequential, perceptual, and spatial predictability. We computed the activity maps unique to switch vs. stay trials and all switch dimensions, then evaluated functional connectivity under these switch conditions by computing the pairwise mutual information functional connectivity (miFC) between regional timeseries. Switch trials exhibited an expected cost in reaction time while sequential predictability produced a significant benefit to task accuracy. Our results showed that switch trials recruited a broader activity map than stay trials, including regions of the DMN, the MDC, and task-positive networks such as visual, somatomotor, dorsal, salience/ventral attention networks. More sequentially predictable trials recruited increased activity in the somatomotor and salience/ventral attention networks. Notably, changes in sequential and perceptual predictability, but not spatial predictability, had significant effects on miFC. Increases in perceptual predictability related to decreased miFC between control, visual, somatomotor, and DMN regions, whereas increases in sequential predictability increased miFC between regions in the same networks, as well as regions within ventral attention/ salience, dorsal attention, limbic, and temporal parietal networks. These results provide novel clues as to how DMN may contribute to executive task performance. Specifically, the improved task performance, unique activity, and increased miFC associated with increased sequential predictability suggest that the DMN may coordinate more strongly with the MDC to generate a temporal schema of upcoming task events, which may attenuate switching costs.

Childhood trauma is linked to abnormal static-dynamic brain topology in adolescents with major depressive disorder

Childhood trauma is a leading risk factor for adolescents developing major depressive disorder (MDD); however, the underlying neuroimaging mechanisms remain unclear. This study aimed to investigate the association among childhood trauma, MDD and brain dysfunctions by combining static and dynamic brain network models. We recruited 46 first-episode drug-naïve adolescent MDD patients with childhood trauma (MDD-CT), 53 MDD patients without childhood trauma (MDD-nCT), and 90 healthy controls (HCs) for resting-state functional magnetic resonance imaging (fMRI) scans; all participants were aged 13-18 years. Compared to the HCs and MDD-nCT groups, the MDD-CT group exhibited significantly higher global and local efficiency in static brain networks and significantly higher temporal correlation coefficients in dynamic brain network models at the whole-brain level, and altered the local efficiency of default mode network (DMN) and temporal correlation coefficients of DMN, salience (SAN), and attention (ATN) networks at the local perspective. Correlation analysis indicated that altered brain network features and clinical symptoms, childhood trauma, and particularly emotional neglect were highly correlated in adolescents with MDD. This study may provide new evidence for the dysconnectivity hypothesis regarding the associations between childhood trauma and MDD in adolescents from the perspectives of both static and dynamic brain topology.

Instructional load induces functional connectivity changes linked to task automaticity and mnemonic preference

Learning new rules rapidly and effectively via instructions is ubiquitous in our daily lives, yet the underlying cognitive and neural mechanisms are complex. Using functional magnetic resonance imaging we examined the effects of different instructional load conditions (4 vs. 10 stimulus-response rules) on functional couplings during rule implementation (always 4 rules). Focusing on connections of lateral prefrontal cortex (LPFC) regions, the results emphasized an opposing trend of load-related changes in LPFC-seeded couplings. On the one hand, during the low-load condition LPFC regions were more strongly coupled with cortical areas mostly assigned to networks such as the fronto-parietal network and the dorsal attention network. On the other hand, during the high-load condition, the same LPFC areas were more strongly coupled with default mode network areas. These results suggest differences in automated processing evoked by features of the instruction and an enduring response conflict mediated by lingering episodic long-term memory traces when instructional load exceeds working memory capacity limits. The ventrolateral prefrontal cortex (VLPFC) exhibited hemispherical differences regarding whole-brain coupling and practice-related dynamics. Left VLPFC connections showed a persistent load-related effect independent of practice and were associated with 'objective' learning success in overt behavioral performance, consistent with a role in mediating the enduring influence of the initially instructed task rules. Right VLPFC's connections, in turn, were more susceptible to practice-related effects, suggesting a more flexible role possibly related to ongoing rule updating processes throughout rule implementation.

Neural representation dynamics reveal computational principles of cognitive task learning

During cognitive task learning, neural representations must be rapidly constructed for novel task performance, then optimized for robust practiced task performance. How the geometry of neural representations changes to enable this transition from novel to practiced performance remains unknown. We hypothesized that practice involves a shift from compositional representations (task-general activity patterns that can be flexibly reused across tasks) to conjunctive representations (task-specific activity patterns specialized for the current task). Functional MRI during learning of multiple complex tasks substantiated this dynamic shift from compositional to conjunctive representations, which was associated with reduced cross-task interference (via pattern separation) and behavioral improvement. Further, we found that conjunctions originated in subcortex (hippocampus and cerebellum) and slowly spread to cortex, extending multiple memory systems theories to encompass task representation learning. The formation of conjunctive representations hence serves as a computational signature of learning, reflecting cortical-subcortical dynamics that optimize task representations in the human brain.

Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia

BACKGROUND

Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis.

METHODS

Forty-seven unmedicated patients with Sz(DSM-IV-TR) underwent clinical evaluation and neuroimaging at baseline and after 3-months of antipsychotic treatment. Resting-state functional MRI was analysed using group-MVPA to derive 5-components. The brain region with significant connectivity pattern changes with antipsychotics was identified, and post-hoc seed-to-voxel analysis was performed to identify connectivity changes and their association with symptom changes.

RESULTS

Connectome-MVPA analysis revealed the connectivity pattern of a cluster localised to left anterior cingulate and paracingulate gyri (ACC/PCG) (peak coordinates:x = -04,y = +30,z = +26;k = 12;cluster-p_FWE=0.002) to differ significantly after antipsychotics. Specifically, its connections with clusters of precuneus/posterior cingulate cortex(PCC) and left inferior temporal gyrus(ITG) correlated with improvement in positive and negative symptoms scores, respectively.

CONCLUSION

ACC/PCG, a hub of the default mode network, seems to mediate the antipsychotic effects in unmedicated Sz. Evaluating causality models with data from randomised controlled design using the MVPA approach would further enhance our understanding of therapeutic connectomics in Sz.

Multitask representations in the human cortex transform along a sensory-to-motor hierarchy

Human cognition recruits distributed neural processes, yet the organizing computational and functional architectures remain unclear. Here, we characterized the geometry and topography of multitask representations across the human cortex using functional magnetic resonance imaging during 26 cognitive tasks in the same individuals. We measured the representational similarity across tasks within a region and the alignment of representations between regions. Representational alignment varied in a graded manner along the sensory-association-motor axis. Multitask dimensionality exhibited compression then expansion along this gradient. To investigate computational principles of multitask representations, we trained multilayer neural network models to transform empirical visual-to-motor representations. Compression-then-expansion organization in models emerged exclusively in a rich training regime, which is associated with learning optimized representations that are robust to noise. This regime produces hierarchically structured representations similar to empirical cortical patterns. Together, these results reveal computational principles that organize multitask representations across the human cortex to support multitask cognition.

Reorganized Hubs of Brain Functional Networks after Acute Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI)-associated damage to hub regions can lead to disrupted modular structures of functional brain networks and may result in widespread cognitive and behavioral deficits. The spatial layout of brain connections and modules is essential for understanding the reorganization of brain networks to trauma. We investigated the roles of hubs in inter-subnetwork information coordination and integration using participation coefficients (PCs) in 74 patients with acute mTBI and 51 matched healthy controls. In some brain networks, such as default mode network (DMN) and frontoparietal network (FPN), mild TBI patients had decreased PC levels, while this measure was saliently increased in patients in other networks, such as the visual network. The hub disruption index was defined as the gradient of a straight line fitted to scatterplots of individual mTBI in participation coefficient versus mean participation coefficient of healthy groups. There was a trend of radical reorganization of some efficient "hub" nodes in patients (κ = -0.15), compared with controls (κ close to 0). The PC of brain hubs can also differentiate mTBI patients from controls with an 88% accuracy, and decreased PC levels in FPN can predict patient' s worse cognitive information processing speed (r = 0.36, p < 0.002) and working memory performance (r = 0.35, p < 0.002). Reduced PC within the DMN was associated with patients' complaints of post-concussion symptoms (r = -0.35, p < 0.002). This evidence suggests a trend of spatial transition of hub profiles in acute mTBI, and graph metrics of PC measures can be used as potential diagnostic biomarkers.

Quiet Trajectories as Neural Building Blocks

Our concept of the neural mechanisms of working memory has recently undergone an upheaval, because of two transformative concepts: multivariate neural state trajectories and the activity-silent hypothesis. I will argue that putting these concepts together raises the difficult problem of "quiet trajectories," where future neural activity is not fully determined by current activity. However, this also promises new building blocks for neural computation.

Global connectivity fingerprints predict the domain generality of multiple-demand regions

A set of distributed cognitive control networks are known to contribute to diverse cognitive demands, yet it is unclear how these networks gain this domain-general capacity. We hypothesized that this capacity is largely due to the particular organization of the human brain's intrinsic network architecture. Specifically, we tested the possibility that each brain region's domain generality is reflected in its level of global (hub-like) intrinsic connectivity as well as its particular global connectivity pattern ("connectivity fingerprint"). Consistent with prior work, we found that cognitive control networks exhibited domain generality as they represented diverse task context information covering sensory, motor response, and logic rule domains. Supporting our hypothesis, we found that the level of global intrinsic connectivity (estimated with resting-state functional magnetic resonance imaging [fMRI]) was correlated with domain generality during tasks. Further, using a novel information fingerprint mapping approach, we found that each cognitive control region's unique rule response profile("information fingerprint") could be predicted based on its unique intrinsic connectivity fingerprint and the information content in regions outside cognitive control networks. Together, these results suggest that the human brain's intrinsic network architecture supports its ability to represent diverse cognitive task information largely via the location of multiple-demand regions within the brain's global network organization.

Theta-phase connectivity between medial prefrontal and posterior areas underlies novel instructions implementation

Implementing novel instructions is a complex and uniquely human cognitive ability, that requires the rapid and flexible conversion of symbolic content into a format that enables the execution of the instructed behavior. Preparing to implement novel instructions, as opposed to their mere maintenance, involves the activation of the instructed motor plans, and the binding of the action information to the specific context in which this should be executed. Recent evidence and prominent computational models suggest that this efficient configuration of the system might involve a central role of frontal theta oscillations in establishing top-down long-range synchronization between distant and task-relevant brain areas. In the present EEG study (human subjects, 30 females, 4 males), we demonstrate that proactively preparing for the implementation of novels instructions, as opposed to their maintenance, involves a strengthened degree of connectivity in the theta frequency range between medial prefrontal and motor/visual areas. Moreover, we replicated previous results showing oscillatory features associated specifically with implementation demands, and extended on them demonstrating the role of theta oscillations in mediating the effect of task demands on behavioral performance. Taken together, these findings support our hypothesis that the modulation of connectivity patterns between frontal and task-relevant posterior brain areas is a core factor in the emergence of a behavior-guiding format from novel instructions.Significance statementEveryday life requires the use and manipulation of currently available information to guide behavior and reach specific goals. In the present study we investigate how the same instructed content elicits different neural activity depending on the task being performed. Crucially, connectivity between medial prefrontal cortex and posterior brain areas is strengthened when novel instructions have to be implemented, rather than simply maintained. This finding suggests that theta oscillations play a role in setting up a dynamic and flexible network of task-relevant regions optimized for the execution of the instructed behavior.

Developmental shifts in computations used to detect environmental controllability

Accurate assessment of environmental controllability enables individuals to adaptively adjust their behavior—exploiting rewards when desirable outcomes are contingent upon their actions and minimizing costly deliberation when their actions are inconsequential. However, it remains unclear how estimation of environmental controllability changes from childhood to adulthood. Ninety participants (ages 8–25) completed a task that covertly alternated between controllable and uncontrollable conditions, requiring them to explore different actions to discover the current degree of environmental controllability. We found that while children were able to distinguish controllable and uncontrollable conditions, accuracy of controllability assessments improved with age. Computational modeling revealed that whereas younger participants’ controllability assessments relied on evidence gleaned through random exploration, older participants more effectively recruited their task structure knowledge to make highly informative interventions. Age-related improvements in working memory mediated this qualitative shift toward increased use of an inferential strategy. Collectively, these findings reveal an age-related shift in the cognitive processes engaged to assess environmental controllability. Improved detection of environmental controllability may foster increasingly adaptive behavior over development by revealing when actions can be leveraged for one’s benefit.

The ability to determine when one’s actions are consequential organizes learning and decision making across the lifespan. However, few studies have examined how the ability to detect control over our environment changes from childhood to adulthood. Here, we leveraged a computational modeling framework to characterize the component learning processes underlying controllability assessment in children, adolescents, and adults. We observed age-related improvements in controllability assessment that stemmed from an increasing ability to represent contingencies between states and actions and to use that knowledge to make informative interventions that yield diagnostic evidence of the current degree of control. Increasing ability to accurately assess environmental controllability may confer greater recognition of opportunities to adaptively pursue rewards through goal-directed action across development.

Social cognitive network neuroscience

Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.

Instruction-based learning: A review

Humans are able to learn to implement novel rules from instructions rapidly, which is termed "instruction-based learning" (IBL). This remarkable ability is very important in our daily life in both learning individually or working as a team, and almost every psychology experiment starts with instructing participants. Many recent progresses have been made in IBL research both psychologically and neuroscientifically. In this review, we discuss the role of language in IBL, the importance of the first trial performance in IBL, why IBL should be considered as a goal-directed behavior, intelligence and IBL, cognitive flexibility and IBL, how behaviorally relevant information is processed in the lateral prefrontal cortex (LPFC), how the lateral frontal cortex (LFC) networks work as a functional hierarchy during IBL, and the cortical and subcortical contributions to IBL. Finally, we develop a neural working model for IBL and provide some sensible directions for future research.

Assessment of Cognitive Reserve using Near Infrared Spectroscopy

Cognitive reserve (CR) is the ability to preserve cognitive functions in the presence of brain pathology. In the context of Alzheimer's disease (AD), patients with higher CR show better cognitive performance relative to brain damage therefore higher CR reduces the risk of dementia. There is a strong need to develop a neurophysiological biomarker of CR given the growing interest in understanding protective brain mechanisms in AD. FMRI studies indicate that frontoparietal network plays an important role in cognitive reserve. We calculated intraregional functional connectivity of lateral prefrontal cortex (FC LPFC) using functional near infrared spectroscopy (fNIRS) in the resting state of 13 healthy individuals who were also assessed for IQ and motoric skills (the Purdue Pegboard test, PPT). FC LPFC was found to positively correlate with IQ (a proxy measure of cognitive reserve) while showing a lack of or negative correlation with the PPT scores. The results demonstrate that the cost-effective, noninvasive and widely applicable fNIRS technology can be used to evaluate cognitive reserve in individuals at risk for and patients with AD with possible numerous applications in the context of healthy aging and other age-related cognitive disorders.

Executive Function Assigns Value to Novel Goal-Congruent Outcomes

People often learn from the outcomes of their actions, even when these outcomes do not involve material rewards or punishments. How does our brain provide this flexibility? We combined behavior, computational modeling, and functional neuroimaging to probe whether learning from abstract novel outcomes harnesses the same circuitry that supports learning from familiar secondary reinforcers. Behavior and neuroimaging revealed that novel images can act as a substitute for rewards during instrumental learning, producing reliable reward-like signals in dopaminergic circuits. Moreover, we found evidence that prefrontal correlates of executive control may play a role in shaping flexible responses in reward circuits. These results suggest that learning from novel outcomes is supported by an interplay between high-level representations in prefrontal cortex and low-level responses in subcortical reward circuits. This interaction may allow for human reinforcement learning over arbitrarily abstract reward functions.

The Generation of Involuntary Mental Imagery in an Ecologically-Valid Task

Laboratory tasks (e.g., the flanker task) reveal that incidental stimuli (e.g., distractors) can reliably trigger involuntary conscious imagery. Can such involuntary effects, involving competing representations, arise during dual-task conditions? Another concern about these laboratory tasks is whether such effects arise in highly ecologically-valid conditions. For example, do these effects arise from tasks involving dynamic stimuli (e.g., simulations of semi-automated driving experiences)? The data from our experiment suggest that the answer to our two questions is yes. Subjects were presented with video footage of the kinds of events that one would observe if one were seated in the driver's seat of a semi-automated vehicle. Before being presented with this video footage, subjects had been trained to respond to street signs according to laboratory techniques that cause stimulus-elicited involuntary imagery. After training, in the Respond condition, subjects responded to the signs; in the Suppress condition, subjects were instructed to not respond to the signs in the video footage. Subjects in the Suppress condition reported involuntary imagery on a substantive proportion of the trials. Such involuntary effects arose even under dual-task conditions (while performing the n-back task or psychomotor vigilance task). The present laboratory task has implications for semi-automated driving, because the safe interaction between driver and vehicle requires that the communicative signals from vehicle to driver be effective at activating the appropriate cognitions and behavioral inclinations. In addition, our data from the dual-task conditions provide constraints for theoretical models of cognitive resources.

Exploring the Link between Novel Task Proceduralization and Motor Simulation

Our ability to generate efficient behavior from novel instructions is critical for our adaptation to changing environments. Despite the absence of previous experience, novel instructed content is quickly encoded into an action-based or procedural format, facilitating automatic task processing. In the current work, we investigated the link between proceduralization and motor simulation, specifically, whether the covert activation of the task-relevant responses is used during the assembly of action-based instructions representations. Across three online experiments, we used a concurrent finger-tapping task to block motor simulation during the encoding of novel stimulus-response (S-R) associations. The overlap between the mappings and the motor task at the response level was manipulated. We predicted a greater impairment at mapping implementation in the overlapping condition, where the mappings' relevant response representations were already loaded by the motor demands, and thus, could not be used in the upcoming task simulation. This hypothesis was robustly supported by the three datasets. Nonetheless, the overlapping effect was not modulated by further manipulations of proceduralization-related variables (preparation demands in Exp.2, mapping novelty in Exp.3). Importantly, a fourth control experiment ruled out that our results were driven by alternative accounts as fatigue or negative priming. Overall, we provided strong evidence towards the involvement of motor simulation during anticipatory task reconfiguration. However, this involvement was rather general, and not restricted to novelty scenarios. Finally, these findings can be also integrated into broader models of anticipatory task control, stressing the role of the motor system during preparation.

Benefits and costs of self-paced preparation of novel task instructions

Rapidly executing novel instructions is a critical ability. However, it remains unclear whether longer preparation of novel instructions improves performance, and if so, whether this link is modulated by performance benefits and costs of preparation. Regarding the first question, we reanalysed previous data on novel instruction implementation and ran Experiment 1. Experiment 1 consisted of multiple mini-blocks, in which participants prepared four novel stimulus-response (S-R) mappings in a self-paced instruction phase. After participants indicated they were ready, one of the four stimuli was presented and they responded. The reanalysis and Experiment 1 showed that longer preparation indeed led to better performance. To examine if preparation was modulated when the benefits of preparation were reduced, we presented the correct response with the stimulus on some trials in Experiments 2 and 3. Preparation was shorter when the probability that the correct response was presented with the stimulus increased. In Experiment 4, we manipulated the costs of preparation by changing the S-R mappings between the instruction and execution phases on some trials. This had only limited effects on preparation time. In conclusion, self-paced preparation of novel instructions comes with performance benefits and costs, and participants adjust their preparation strategy to the task context.

Automatic effects of instructions: a tale of two paradigms

When examining rapid instructed task learning behaviorally, one out of two paradigms is usually used, the Inducer-Diagnostic (I-D) and the NEXT paradigm. Even though both paradigms are supposed to examine the same phenomenon of Automatic Effect of Instructions (AEI), there are some meaningful differences between them, notably in the size of the AEI. In the current work, we examined, in two pre-registered studies, the potential reasons for these differences in AEI size. Study 1 examined the influence of the data-analytic approach by comparing two existing relatively large data-sets, one from each paradigm (Braem et al., in Mem Cogn 47:1582–1591, 2019; Meiran et al., in Neuropsychologia 90:180–189, 2016). Study 2 focused on the influence of instruction type (concrete, as in NEXT, and abstract, as in I-D) and choice complexity of the task in which AEI-interference is assessed. We did that while using variants of the NEXT paradigm, some with modifications that approximated it to the I-D paradigm. Results from Study 1 indicate that the data-analytic approach partially explains the differences between the paradigms in terms of AEI size. Still, the paradigms remained different with respect to individual differences and with respect to AEI size in the first step following the instructions. Results from Study 2 indicate that Instruction type and the choice complexity in the phase in which AEI is assessed do not influence AEI size, or at least not in the expected direction. Theoretical and study-design implications are discussed.

Learning the Abstract General Task Structure in a Rapidly Changing Task Content

The ability to learn abstract generalized structures of tasks is crucial for humans to adapt to changing environments and novel tasks. In a series of five experiments, we investigated this ability using a Rapid Instructed Task Learning paradigm (RITL) comprising short miniblocks, each involving two novel stimulus-response rules. Each miniblock included (a) instructions for the novel stimulus-response rules, (b) a NEXT phase involving a constant (familiar) intervening task (0–5 trials), (c) execution of the newly instructed rules (2 trials). The results show that including a NEXT phase (and hence, a prospective memory demand) led to relatively more robust abstract learning as indicated by increasingly faster responses with experiment progress. Multilevel modeling suggests that the prospective memory demand was just another aspect of the abstract task structure which has been learned.

Role of verbal working memory in rapid procedural acquisition of a choice response task

How quickly are instructions for a task translated into an effective task-set? If declarative working memory (dWM) is used to maintain a task's S-R rules until practice compiles them adequately into procedural memory, variables that affect retrieval from dWM should influence task performance while it is still dependent on dWM. Participants were trained on a series of 6-choice RT tasks, with a 1:1 mapping from object pictures to keys. In Experiments 1 and 2, an instruction phase - presentation of the S-R rules one by one -was followed by test trials. The phonological similarity of the objects' names significantly affected performance only during the first few encounters with the stimuli. Serial position effects were also consistent with retrieval from verbal dWM during those early trials. In Experiment 3, instruction onon the S-R rules was omitted, so participants had to learn the S-R mappings by trial and error alone; the effect of phonological similarity lasted longer, but still disappeared after a dozen encounters with each stimulus. Experiment 4 showed that instructions and just four trials of practice per S-R rule were sufficient to form a persisting representation of the S-R rules robust enough to interfere with later acquisition of a competing S-R rule after several minutes spent acquiring other task-sets. An effective and lasting task-set is rapidly compiled into procedural memory through instruction and early feedback; verbal dWM plays little role thereafter.

Resting heart rate variability modulates the effects of concurrent working memory load on affective startle modification

Vagally mediated heart rate variability (vmHRV) is thought to index top-down control processes in emotion regulation. According to the Generalized Unsafety Theory of Stress (GUTS), resting vmHRV indexes top-down resources that are needed to inhibit subcortical threat circuits, which is important for context-appropriate affective responding. Although this notion has been supported by studies of affective startle (SR) modification, direct evidence that top-down resources are the linking mechanism between vmHRV and context-appropriate affective responding has been lacking. To investigate this possible mechanism, college-aged participants (n = 92) were recruited to complete a picture viewing task and a concurrent working memory (WM) task. Concurrent WM load was manipulated, and the auditory SR stimulus was delivered while viewing affective pictures. Electrocardiography and electromyography were recorded to assess vmHRV and SR eyeblink, respectively. Results showed that WM load attenuated affective SR modification. As expected, the attenuating effects of load on affective SR modification were stronger among low vmHRV relative to high vmHRV individuals, indicating that vmHRV is linked to context-appropriate affective responding through the mechanism of top-down resources. These results support the GUTS and suggest that atypical affective responding among low vmHRV individuals is attributed to the lack of WM resources. Our findings highlight the relation between vmHRV and top-down resources that have been implicated in emotion regulation and contribute to a better understanding of emotion dysregulation in psychopathology.

Neural Mechanisms of Observational Learning: A Neural Working Model

Humans and some animal species are able to learn stimulus-response (S-R) associations by observing others' behavior. It saves energy and time and avoids the danger of trying the wrong actions. Observational learning (OL) depends on the capability of mapping the actions of others into our own behaviors, processing outcomes, and combining this knowledge to serve our goals. Observational learning plays a central role in the learning of social skills, cultural knowledge, and tool use. Thus, it is one of the fundamental processes in which infants learn about and from adults (Byrne and Russon, 1998). In this paper, we review current methodological approaches employed in observational learning research. We highlight the important role of the prefrontal cortex and cognitive flexibility to support this learning process, develop a new neural working model of observational learning, illustrate how imitation relates to observational learning, and provide directions for future research.

Disrupted Salience and Cingulo-Opercular Network Connectivity During Impaired Rapid Instructed Task Learning in Schizophrenia

Rapid instructed task learning (RITL) is the uniquely human ability to transform task information into goal-directed behavior without relying on trial-and-error learning. RITL is a core cognitive process supported by functional brain networks. In patients with schizophrenia, RITL ability is impaired, but the role of functional network connectivity in these RITL deficits is unknown. We investigated task-based connectivity of eight a priori network pairs in participants with schizophrenia (n = 29) and control participants (n = 31) during the performance of an RITL task. Multivariate pattern analysis was used to determine which network connectivity patterns predicted diagnostic group. Of all network pairs, only the connectivity between the cingulo-opercular network (CON) and salience network (SAN) during learning classified patients and control participants with significant accuracy (80%). CON-SAN connectivity during learning was significantly associated with task performance in participants with schizophrenia. These findings suggest that impaired interactions between identification of salient stimuli and maintenance of task goals contributes to RITL deficits in participants with schizophrenia.

Neural oscillations track the maintenance and proceduralization of novel instructions

Humans are capable of flexibly converting symbolic instructions into novel behaviors. Previous evidence and theoretical models suggest that the implementation of a novel instruction requires the reformatting of its declarative content into an action-oriented code optimized for the execution of the instructed behavior. While neuroimaging research focused on identifying the brain areas involved in such a process, the temporal and electrophysiological mechanisms remain poorly understood. These mechanisms, however, can provide information about the specific cognitive processes that characterize the proceduralization of information. In the present study, we recorded EEG activity while we asked participants to either simply maintain declaratively the content of novel S-R mappings or to proactively prepare for their implementation. By means of time-frequency analyses, we isolated the oscillatory features specific to the proceduralization of instructions. Implementation of the instructed mappings elicited stronger theta activity over frontal electrodes and suppression in mu and beta activity over central electrodes. On the contrary, activity in the alpha band, which has been shown to track the attentional deployment to task-relevant items, showed no differences between tasks. Together, these results support the idea that proceduralization of information is characterized by specific component processes such as orchestrating complex task settings and configuring the motor system that are not observed when instructions are held in a declarative format.

Training-dependent transfer within a set of nested tasks

Extended practice on a particular cognitive task can boost the performance of other tasks, even though they themselves have not been practised. This transfer of benefits appears to be specific, occurring most when tasks are very similar to those being trained. But what type of similarity is most important for predicting transfer? This question is addressed with a tightly controlled randomised design, with a relatively large sample (N = 175) and an adaptive control group. We created a hierarchical set of nested assessment tasks. Participants then trained on two of the tasks: one was relatively "low" in the hierarchy requiring just simultaneous judgements of shapes' spikiness, whereas the other was relatively "high" requiring delayed judgements of shapes' spikiness or number of spikes in a switching paradigm. Using the full complement of nested tasks before and after training, we could then test whether and how these "low" and "high" training effects cascade through the hierarchy. For both training groups, relative to the control, whether or not an assessment task shared a single specific feature was the best predictor of transfer patterns. For the low-level training group, the overall proportion of feature overlap also significantly predicted transfer, but the same was not true for the high-level training group. Finally, pre-training between-task correlations were not predictive of the pattern of transfer for either group. Together these findings provide an experimental exploration of the specificity of transfer and establish the nature of task overlap that is crucial for the transfer of performance improvements.

Examining the Trainability and Transferability of Working-Memory Gating Policies

Internal working memory (WM) gating control policies have been suggested to constitute a critical component of task-sets that can be learned and transferred to very similar task contexts (Bhandari and Badre (Cognition, 172, 33–43, 2018). Here, we attempt to expand these findings, examining whether such control policies can be also trained and transferred to other untrained cognitive control tasks, namely to task switching and AX-CPT. To this end, a context-processing WM task was used for training, allowing to manipulate either input (i.e., top-down selective entry of information into WM) or output (i.e., bottom-up selective retrieval of WM) gating control policies by employing either a context-first (CF) or context-last (CL) task structure, respectively. In this task, two contextual cues were each associated with two different stimuli. In CF condition, each trial began with a contextual cue, determining which of the two subsequent stimuli is target relevant. In contrast, in the CL condition the contextual cue appeared last, preceded by a target and non-target stimulus successively. Participants completed a task switching baseline assessment, followed by one practice and six training blocks with the WM context-processing training task. After completing training, task-switching and AX-CPT transfer blocks were administrated, respectively. As hypothesized, compared to CL training condition, CF training led to improved task-switching performance. However, contrary to our predictions, training type did not influence AX-CPT performance. Taken together, the current results provide further evidence that internal control policies are (1) inherent element of task-sets, also in task switching and (2) independent of S-R mappings. However, these results need to be cautiously interpreted due to baseline differences in task-switching performance between the conditions (overall slower RTs in the CF condition). Importantly though, our results open a new venue for the realm of cognitive enhancement, pointing here for the first time to the potential of control policies training in promoting wider transfer effects.

Brain Mechanisms Supporting Flexible Cognition and Behavior in Adolescents With Autism Spectrum Disorder

Cognitive flexibility enables appropriate responses to a changing environment and is associated with positive life outcomes. Adolescence, with its increased focus on transitioning to independent living, presents particular challenges for youths with autism spectrum disorder (ASD) who often struggle to behave in a flexible way when faced with challenges. This review focuses on brain mechanisms underlying the development of flexible cognition during adolescence and how these neural systems are affected in ASD. Neuroimaging studies of task switching and set-shifting provide evidence for atypical lateral frontoparietal and midcingulo-insular network activation during cognitive flexibility task performance in individuals with ASD. Recent work also examines how intrinsic brain network dynamics support flexible cognition. These dynamic functional connectivity studies provide evidence for alterations in the number of transitions between brain states, as well as hypervariability of functional connections in adolescents with ASD. Future directions for the field include addressing issues related to measurement of cognitive flexibility using a combination of metrics with ecological and construct validity. Heterogeneity of executive function ability in ASD must also be parsed to determine which individuals will benefit most from targeted training to improve flexibility. The influence of pubertal hormones on brain network development and cognitive maturation in adolescents with ASD is another area requiring further exploration. Finally, the intriguing possibility that bilingualism might be associated with preserved cognitive flexibility in ASD should be further examined. Addressing these open questions will be critical for future translational neuroscience investigations of cognitive and behavioral flexibility in adolescents with ASD.

Frontoparietal action-oriented codes support novel instruction implementation

A key aspect of human cognitive flexibility concerns the ability to convert complex symbolic instructions into novel behaviors. Previous research proposes that this transformation is supported by two neurocognitive states: an initial declarative maintenance of task knowledge, and an implementation state necessary for optimal task execution. Furthermore, current models predict a crucial role of frontal and parietal brain regions in this process. However, whether declarative and procedural signals independently contribute to implementation remains unknown. We report the results of an fMRI experiment in which participants executed novel instructed stimulus-response associations. We then used a pattern-tracking procedure to quantify the contribution of format-unique signals during instruction implementation. This revealed independent procedural and declarative representations of novel S-Rs in frontoparietal areas, prior to execution. Critically, the degree of procedural activation predicted subsequent behavioral performance. Altogether, our results suggest an important contribution of frontoparietal regions to the neural architecture that regulates cognitive flexibility.

Primarily Disrupted Default Subsystems Cause Impairments in Inter-system Interactions and a Higher Regulatory Burden in Alzheimer's Disease

Background: Intrinsically organized large-scale brain networks and their interactions support complex cognitive function. Investigations suggest that the default network (DN) is the earliest disrupted network and that the frontoparietal control network (FPCN) and dorsal attention network (DAN) are subsequently impaired in Alzheimer's disease (AD). These large-scale networks comprise different subsystems (DN: medial temporal lobe (MTL), dorsomedial prefrontal cortex (DM) subsystems and a Core; FPCN: FPCNA and FPCNB). Our previous research has indicated that different DN subsystems are not equally damaged in AD. However, changes in the patterns of interactions among these large-scale network subsystems and the underlying cause of the alterations in AD remain unclear. We hypothesized that disrupted DN subsystems cause specific impairments in inter-system interactions and a higher regulatory burden for the FPCNA. Method: To test this hypothesis, Granger causality analysis (GCA) was performed to explore effective functional connectivity (FC) pattern of these networks. The regional information flow strength (IFS) was calculated and compared across groups to explore changes in the subsystems and their inter-system interactions and the relationship between them. To investigate specific inter-system changes, we summed the inter-system IFS and performed correlation analyses of the bidirectional inter-system IFS, which was compared across groups. Additionally, correlation analyses of dynamic effective FC patterns were performed to reveal alterations in the temporal co-evolution of sets of inter-subsystem interactions. Furthermore, we used partial correlation analysis to quantify the FPCN's regulatory effects. Finally, we applied a support vector machine (SVM) linear classifier to probe which network most effectively discriminated patients from controls. Results: Compared with controls, AD patients showed a decreased intra-DN regional IFS, which was significantly related to the inter-network's IFS. The IFS between the DN subsystems and FPCN subsystems/DAN decreased. Critically, the correlation values of the decreased bidirectional IFS between the DN subsystems and FPCNA diminished. Additionally, the Core and DM play pivotal roles in disordered temporal co-evolution. Furthermore, the FPCNA showed enhanced regulation of the Core. Finally, the MTL subsystem and Core were effective at discriminating patients from controls. Conclusion: The predominantly disrupted DN subsystems caused impaired inter-system interactions and created a higher regulatory burden for the FPCNA.

Flexible Coordinator and Switcher Hubs for Adaptive Task Control

Functional connectivity (FC) studies have identified at least two large-scale neural systems that constitute cognitive control networks, the frontoparietal network (FPN) and cingulo-opercular network (CON). Control networks are thought to support goal-directed cognition and behavior. It was previously shown that the FPN flexibly shifts its global connectivity pattern according to task goal, consistent with a "flexible hub" mechanism for cognitive control. Our aim was to build on this finding to develop a functional cartography (a multimetric profile) of control networks in terms of dynamic network properties. We quantified network properties in (male and female) humans using a high-control-demand cognitive paradigm involving switching among 64 task sets. We hypothesized that cognitive control is enacted by the FPN and CON via distinct but complementary roles reflected in network dynamics. Consistent with a flexible "coordinator" mechanism, FPN connections were varied across tasks, while maintaining within-network connectivity to aid cross-region coordination. Consistent with a flexible "switcher" mechanism, CON regions switched to other networks in a task-dependent manner, driven primarily by reduced within-network connections to other CON regions. This pattern of results suggests FPN acts as a dynamic, global coordinator of goal-relevant information, while CON transiently disbands to lend processing resources to other goal-relevant networks. This cartography of network dynamics reveals a dissociation between two prominent cognitive control networks, suggesting complementary mechanisms underlying goal-directed cognition.SIGNIFICANCE STATEMENT Cognitive control supports a variety of behaviors requiring flexible cognition, such as rapidly switching between tasks. Furthermore, cognitive control is negatively impacted in a variety of mental illnesses. We used tools from network science to characterize the implementation of cognitive control by large-scale brain systems. This revealed that two systems, the frontoparietal (FPN) and cingulo-opercular (CON) networks, have distinct but complementary roles in controlling global network reconfigurations. The FPN exhibited properties of a flexible coordinator (orchestrating task changes), while CON acted as a flexible switcher (switching specific regions to other systems to lend processing resources). These findings reveal an underlying distinction in cognitive processes that may be applicable to clinical, educational, and machine learning work targeting cognitive flexibility.

Neural Responses to a Putative Set-shifting Task in Children with Autism Spectrum Disorder

While much progress has been made toward understanding the neurobiology of social and communication deficits associated with autism spectrum disorder (ASD), less is known regarding the neurobiological basis of restricted and repetitive behaviors (RRBs) central to the ASD diagnosis. Symptom severity for RRBs in ASD is associated with cognitive inflexibility. Thus, understanding the neural mechanisms underlying cognitive inflexibility in ASD is critical for tailoring therapies to treat this understudied yet pervasive symptom. Here we used a set-shifting paradigm adopted from the developmental cognitive neuroscience literature involving flexible switching between stimulus categories to examine task performance and neural responses in children with ASD. Behaviorally, we found little evidence for group differences in performance on the set-shifting task. Compared with typically developing children, children with ASD exhibited greater activation of the parahippocampal gyrus during performance on trials requiring switching. These findings suggest that children with ASD may need to recruit memory-based neural systems to a greater degree when learning to flexibly associate stimuli with responses. LAY SUMMARY: Children with autism often struggle to behave in a flexible way when faced with unexpected challenges. We examined brain responses during a task thought to involve flexible thinking and found that compared with typically developing children, those with autism relied more on brain areas involved in learning and memory to complete the task. This study helps us to understand what types of cognitive tasks are best suited for exploring the neural basis of cognitive flexibility in children with autism. Autism Res 2020, 13: 1501-1515. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.

How Abstract (Non-embodied) Linguistic Representations Augment Cognitive Control

Recent scholarship emphasizes the scaffolding role of language for cognition. Language, it is claimed, is a cognition-enhancing niche (Clark, 2006), a programming tool for cognition (Lupyan and Bergen, 2016), even neuroenhancement (Dove, 2019) and augments cognitive functions such as memory, categorization, cognitive control, and meta-cognitive abilities ("thinking about thinking"). Yet, the notion that language enhances or augments cognition, and in particular, cognitive control does not easily fit in with embodied approaches to language processing, or so we will argue. Accounts aiming to explain how language enhances various cognitive functions often employ a notion of abstract representation. Yet, embodied approaches to language processing have it that language processing crucially, according to some accounts even exclusively, involves embodied, modality-specific, i.e., non-abstract representations. In coming to understand a particular phrase or sentence, a prior experience has to be simulated or reenacted. The representation thus activated is embodied (modality-specific) as sensorimotor regions of the brain are thereby recruited. In this paper, we will first discuss the notion of representation, clarify what it takes for a representation to be embodied or abstract, and distinguish between conceptual and (other) linguistic representations. We will then put forward a characterization of cognitive control and examine its representational infrastructure. The remainder of the paper will be devoted to arguing that language augments cognitive control. To that end, we will draw on two lines of research, which investigate how language augments cognitive control: (i) research on the availability of linguistic labels and (ii) research on the active usage of a linguistic code, specifically, in inner speech. Eventually, we will argue that the cognition-enhancing capacity of language can be explained once we assume that it provides us with (a) abstract, non-embodied representations and with (b) abstract, sparse linguistic representations that may serve as easy-to-manipulate placeholders for fully embodied or otherwise more detailed representations.

Adolescents at clinical high risk for psychosis show qualitatively altered patterns of activation during rule learning

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Novel investigation of novel rule learning in psychosis risk.

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Failed to replicate previous study comparing novel and practiced rule learning.

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No significant group differences, but effect size comparison revealed differences.

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Results suggest that psychosis risk group may rely on different rule retrieval strategies.

Background

The ability to flexibly apply rules to novel situations is a critical aspect of adaptive human behavior. While executive function deficits are known to appear early in the course of psychosis, it is unclear which specific facets are affected. Identifying whether rule learning is impacted at the early stages of psychosis is necessary for truly understanding the etiology of psychosis and may be critical for designing novel treatments. Therefore, we examined rule learning in healthy adolescents and those meeting criteria for clinical high risk (CHR) for psychosis.

Methods

24 control and 22 CHR adolescents underwent rapid, high-resolution fMRI while performing a paradigm which required them to apply novel or practiced task rules.

Results

Previous work has suggested that practiced rules rely on rostrolateral prefrontal cortex (RLPFC) during rule encoding and dorsolateral prefrontal cortex (DLPFC) during task performance, while novel rules show the opposite pattern. We failed to replicate this finding, with greater activity for novel rules during performance. Comparing the HC and CHR group, there were no statistically significant effects, but an effect size analysis found that the CHR group showed less activation during encoding and greater activation during performance. This suggests the CHR group may use less efficient reactive control to retrieve task rules at the time of task performance, rather than proactively during rule encoding.

Conclusions

These findings suggest that flexibility is qualitatively altered in the clinical high risk state, however, more data is needed to determine whether these deficits predict disease progression.

Investigating the effect of trustworthiness on instruction-based reflexivity

Unlike other species, humans are capable of rapidly learning new behavior from a single instruction. While previous research focused on the cognitive processes underlying the rapid, automatic implementation of instructions, the fundamentally social nature of instruction following has remained largely unexplored. Here, we investigated whether instructor trustworthiness modulates instruction implementation using both explicit and reflexive measures. In a first preregistered study, we validated a new paradigm to manipulate the perceived trustworthiness of two different virtual characters and showed that such a manipulation reliably induced implicit associations between the virtual characters and trustworthiness attributes. Moreover, we show that trustworthy instructors are followed more frequently and faster. In two additional preregistered experiments, we tested if trustworthiness towards the instructor influenced the cognitive processes underlying instruction implementation. While we show that verbally conveyed instructions led to automatic instruction implementation, this effect was not modulated by the trustworthiness of the instructor. Thus, we succeeded to design and validate a novel trustworthiness manipulation (Experiment 1) and to create a social variant of the instruction-based reflexivity paradigm (Experiments 2 and 3). However, this instruction-based reflexivity effect was not modulated by the instructors' trustworthiness.

Declarative and procedural working memory updating processes are mutually facilitative

Executive function, or cognitive control, describes the ability to guide information processing in line with internal goals, but the nature of-and relationship between-the component processes supporting this ability remains poorly understood. Two key components of cognitive control are thought to be the regulation of the declarative contents of working memory (WM) and the selection of task sets, or procedural rules that determine how declarative items are employed. Factor-analytic studies have suggested that updating the items held in WM and updating task sets are cognitively distinct, but interrelated, core domains of executive function. However, the precise relationship between these processes remains unknown, since they have rarely been tested simultaneously in a single task. In the present study, we devised a novel method of independently manipulating declarative item-updating and procedural task-updating processes in WM. Across two experiments, we found that the updating of declarative and procedural WM representations interacted subadditively, suggesting they are not constrained by a common processing bottleneck. Moreover, in a third experiment, we found that updating two declarative items in WM simultaneously did not incur a behavioral cost in response time above and beyond the cost of one item alone. Taken together, our results provide new evidence that the updating of information in declarative and procedural WM is mutually facilitative, such that opening the gate for updating declarative content reduces the time needed to update procedural content, and vice versa.

Power of instructions for task implementation: superiority of explicitly instructed over inferred rules

"Power of instructions" originally referred to automatic response activation associated with instructed rules, but previous examination of the power of instructed rules in actual task implementation has been limited. Typical tasks involve both explicit aspects (e.g., instructed stimulus-response mapping rules) and implied, yet easily inferred aspects (e.g., be ready, attend to error beeps) and it is unknown if inferred aspects also become readily executable like their explicitly instructed counterparts. In each mini-block of our paradigm we introduced a novel two-choice task. In the instructions phase, one stimulus was explicitly mapped to a response; whereas the other stimulus' response mapping had to be inferred. Results show that, in most cases, explicitly instructed rules were implemented more efficiently than inferred rules, but this advantage was observed only in the first trial following instructions (though not in the first implementation of the rules), which suggests that the entire task set was implemented in the first trial. Theoretical implications are discussed.

The Instructed Task-Switch Evaluation Effect: Is the Instruction to Switch Tasks Sufficient to Dislike Task Switch Cues?

It is often argued that people dislike situations in which there is conflict requiring cognitive control, possibly because it is effortful to resolve this conflict. In a recent study, Vermeylen, Braem, and Notebaert (2019) provided evidence for this idea in the context of task switching. They observed that participants evaluated cues signaling a task switch more negatively than cues signaling a task repetition in a task switching paradigm. The present study examined whether this evaluative bias can be observed also on the basis of mere instructions. We instructed participants that two non-words would either signal the requirement to switch or to repeat tasks in an upcoming task switching block, which was actually never administered. In Experiment 1, we did not observe more positive implicit or explicit evaluations of the instructed task repetition compared to the task switch cue. In Experiment 2, participants first completed a task switching block in which a first pair of transition cues were used. We then provided task switching instructions that described the signaling function of a second pair of cues, which would be used in an upcoming (but never administered) second task switching block. Participants showed a clear preference for both instructed and experienced task repetition cues on explicit but not on implicit evaluations. Experiment 3 replicated the instructed task-switch evaluation effect on explicit evaluations in the context of prior task experience (but not without prior experience) and extended it to implicit evaluations. We discuss theoretical implications and potential explanations of this task-switch evaluation effect.

Discovering the Computational Relevance of Brain Network Organization

Understanding neurocognitive computations will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. We review recent advances in linking empirical and simulated brain network organization with cognitive information processing. Building on these advances, we offer a new framework for understanding the role of connectivity in cognition: network coding (encoding/decoding) models. These models utilize connectivity to specify the transfer of information via neural activity flow processes, successfully predicting the formation of cognitive representations in empirical neural data. The success of these models supports the possibility that localized neural functions mechanistically emerge (are computed) from distributed activity flow processes that are specified primarily by connectivity patterns.